lambdal/text-to-pokemon
Prediction
lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912IDn2vfz327yvgnpnrcbecukyqvh4StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- Yoda
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- "50"
{ "prompt": "Yoda", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", { input: { prompt: "Yoda", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: "50" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", input={ "prompt": "Yoda", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": "50" } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", "input": { "prompt": "Yoda", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-09-21T09:44:49.500849Z", "created_at": "2022-09-21T09:44:19.304339Z", "data_removed": false, "error": null, "id": "n2vfz327yvgnpnrcbecukyqvh4", "input": { "prompt": "Yoda", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": "50" }, "logs": "Using seed: 4358\n\n 0%| | 0/51 [00:00<?, ?it/s]\n 2%|▏ | 1/51 [00:00<00:05, 9.21it/s]\n 4%|▍ | 2/51 [00:00<00:10, 4.84it/s]\n 6%|▌ | 3/51 [00:00<00:11, 4.22it/s]\n 8%|▊ | 4/51 [00:00<00:12, 3.89it/s]\n 10%|▉ | 5/51 [00:01<00:12, 3.79it/s]\n 12%|█▏ | 6/51 [00:01<00:12, 3.74it/s]\n 14%|█▎ | 7/51 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/51 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/51 [00:02<00:11, 3.64it/s]\n 20%|█▉ | 10/51 [00:02<00:11, 3.64it/s]\n 22%|██▏ | 11/51 [00:02<00:11, 3.62it/s]\n 24%|██▎ | 12/51 [00:03<00:10, 3.61it/s]\n 25%|██▌ | 13/51 [00:03<00:10, 3.61it/s]\n 27%|██▋ | 14/51 [00:03<00:10, 3.61it/s]\n 29%|██▉ | 15/51 [00:03<00:10, 3.59it/s]\n 31%|███▏ | 16/51 [00:04<00:09, 3.59it/s]\n 33%|███▎ | 17/51 [00:04<00:09, 3.60it/s]\n 35%|███▌ | 18/51 [00:04<00:09, 3.60it/s]\n 37%|███▋ | 19/51 [00:05<00:08, 3.59it/s]\n 39%|███▉ | 20/51 [00:05<00:08, 3.60it/s]\n 41%|████ | 21/51 [00:05<00:08, 3.60it/s]\n 43%|████▎ | 22/51 [00:05<00:08, 3.60it/s]\n 45%|████▌ | 23/51 [00:06<00:07, 3.61it/s]\n 47%|████▋ | 24/51 [00:06<00:07, 3.60it/s]\n 49%|████▉ | 25/51 [00:06<00:07, 3.60it/s]\n 51%|█████ | 26/51 [00:07<00:06, 3.60it/s]\n 53%|█████▎ | 27/51 [00:07<00:06, 3.60it/s]\n 55%|█████▍ | 28/51 [00:07<00:06, 3.59it/s]\n 57%|█████▋ | 29/51 [00:07<00:06, 3.59it/s]\n 59%|█████▉ | 30/51 [00:08<00:05, 3.60it/s]\n 61%|██████ | 31/51 [00:08<00:05, 3.60it/s]\n 63%|██████▎ | 32/51 [00:08<00:05, 3.60it/s]\n 65%|██████▍ | 33/51 [00:08<00:04, 3.60it/s]\n 67%|██████▋ | 34/51 [00:09<00:04, 3.61it/s]\n 69%|██████▊ | 35/51 [00:09<00:04, 3.60it/s]\n 71%|███████ | 36/51 [00:09<00:04, 3.61it/s]\n 73%|███████▎ | 37/51 [00:10<00:03, 3.60it/s]\n 75%|███████▍ | 38/51 [00:10<00:03, 3.60it/s]\n 76%|███████▋ | 39/51 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 40/51 [00:10<00:03, 3.61it/s]\n 80%|████████ | 41/51 [00:11<00:02, 3.60it/s]\n 82%|████████▏ | 42/51 [00:11<00:02, 3.60it/s]\n 84%|████████▍ | 43/51 [00:11<00:02, 3.61it/s]\n 86%|████████▋ | 44/51 [00:12<00:01, 3.62it/s]\n 88%|████████▊ | 45/51 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 46/51 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 47/51 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 48/51 [00:13<00:00, 3.62it/s]\n 96%|█████████▌| 49/51 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 50/51 [00:13<00:00, 3.62it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.63it/s]\n100%|██████████| 51/51 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.236785, "total_time": 30.19651 }, "output": [ "https://replicate.delivery/mgxm/4d12a241-fd84-4b0a-8321-80dd8c6ae784/out-0.png" ], "started_at": "2022-09-21T09:44:34.264064Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n2vfz327yvgnpnrcbecukyqvh4", "cancel": "https://api.replicate.com/v1/predictions/n2vfz327yvgnpnrcbecukyqvh4/cancel" }, "version": "3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912" }Generated inUsing seed: 4358 0%| | 0/51 [00:00<?, ?it/s] 2%|▏ | 1/51 [00:00<00:05, 9.21it/s] 4%|▍ | 2/51 [00:00<00:10, 4.84it/s] 6%|▌ | 3/51 [00:00<00:11, 4.22it/s] 8%|▊ | 4/51 [00:00<00:12, 3.89it/s] 10%|▉ | 5/51 [00:01<00:12, 3.79it/s] 12%|█▏ | 6/51 [00:01<00:12, 3.74it/s] 14%|█▎ | 7/51 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/51 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/51 [00:02<00:11, 3.64it/s] 20%|█▉ | 10/51 [00:02<00:11, 3.64it/s] 22%|██▏ | 11/51 [00:02<00:11, 3.62it/s] 24%|██▎ | 12/51 [00:03<00:10, 3.61it/s] 25%|██▌ | 13/51 [00:03<00:10, 3.61it/s] 27%|██▋ | 14/51 [00:03<00:10, 3.61it/s] 29%|██▉ | 15/51 [00:03<00:10, 3.59it/s] 31%|███▏ | 16/51 [00:04<00:09, 3.59it/s] 33%|███▎ | 17/51 [00:04<00:09, 3.60it/s] 35%|███▌ | 18/51 [00:04<00:09, 3.60it/s] 37%|███▋ | 19/51 [00:05<00:08, 3.59it/s] 39%|███▉ | 20/51 [00:05<00:08, 3.60it/s] 41%|████ | 21/51 [00:05<00:08, 3.60it/s] 43%|████▎ | 22/51 [00:05<00:08, 3.60it/s] 45%|████▌ | 23/51 [00:06<00:07, 3.61it/s] 47%|████▋ | 24/51 [00:06<00:07, 3.60it/s] 49%|████▉ | 25/51 [00:06<00:07, 3.60it/s] 51%|█████ | 26/51 [00:07<00:06, 3.60it/s] 53%|█████▎ | 27/51 [00:07<00:06, 3.60it/s] 55%|█████▍ | 28/51 [00:07<00:06, 3.59it/s] 57%|█████▋ | 29/51 [00:07<00:06, 3.59it/s] 59%|█████▉ | 30/51 [00:08<00:05, 3.60it/s] 61%|██████ | 31/51 [00:08<00:05, 3.60it/s] 63%|██████▎ | 32/51 [00:08<00:05, 3.60it/s] 65%|██████▍ | 33/51 [00:08<00:04, 3.60it/s] 67%|██████▋ | 34/51 [00:09<00:04, 3.61it/s] 69%|██████▊ | 35/51 [00:09<00:04, 3.60it/s] 71%|███████ | 36/51 [00:09<00:04, 3.61it/s] 73%|███████▎ | 37/51 [00:10<00:03, 3.60it/s] 75%|███████▍ | 38/51 [00:10<00:03, 3.60it/s] 76%|███████▋ | 39/51 [00:10<00:03, 3.61it/s] 78%|███████▊ | 40/51 [00:10<00:03, 3.61it/s] 80%|████████ | 41/51 [00:11<00:02, 3.60it/s] 82%|████████▏ | 42/51 [00:11<00:02, 3.60it/s] 84%|████████▍ | 43/51 [00:11<00:02, 3.61it/s] 86%|████████▋ | 44/51 [00:12<00:01, 3.62it/s] 88%|████████▊ | 45/51 [00:12<00:01, 3.61it/s] 90%|█████████ | 46/51 [00:12<00:01, 3.60it/s] 92%|█████████▏| 47/51 [00:12<00:01, 3.62it/s] 94%|█████████▍| 48/51 [00:13<00:00, 3.62it/s] 96%|█████████▌| 49/51 [00:13<00:00, 3.61it/s] 98%|█████████▊| 50/51 [00:13<00:00, 3.62it/s] 100%|██████████| 51/51 [00:13<00:00, 3.63it/s] 100%|██████████| 51/51 [00:13<00:00, 3.65it/s]Prediction
lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912IDfd474nxl75hp5bbe6fpx4vjtq4StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- Totoro
- num_outputs
- "4"
- guidance_scale
- 7.5
- num_inference_steps
- "50"
{ "prompt": "Totoro", "num_outputs": "4", "guidance_scale": 7.5, "num_inference_steps": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", { input: { prompt: "Totoro", num_outputs: "4", guidance_scale: 7.5, num_inference_steps: "50" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", input={ "prompt": "Totoro", "num_outputs": "4", "guidance_scale": 7.5, "num_inference_steps": "50" } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run lambdal/text-to-pokemon using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "lambdal/text-to-pokemon:3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912", "input": { "prompt": "Totoro", "num_outputs": "4", "guidance_scale": 7.5, "num_inference_steps": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-09-21T09:46:30.317307Z", "created_at": "2022-09-21T09:45:31.967108Z", "data_removed": false, "error": null, "id": "fd474nxl75hp5bbe6fpx4vjtq4", "input": { "prompt": "Totoro", "num_outputs": "4", "guidance_scale": 7.5, "num_inference_steps": "50" }, "logs": "Using seed: 52884\n\n 0%| | 0/51 [00:00<?, ?it/s]\n 2%|▏ | 1/51 [00:00<00:19, 2.60it/s]\n 4%|▍ | 2/51 [00:01<00:37, 1.30it/s]\n 6%|▌ | 3/51 [00:02<00:42, 1.13it/s]\n 8%|▊ | 4/51 [00:03<00:43, 1.07it/s]\n 10%|▉ | 5/51 [00:04<00:44, 1.03it/s]\n 12%|█▏ | 6/51 [00:05<00:44, 1.01it/s]\n 14%|█▎ | 7/51 [00:06<00:44, 1.00s/it]\n 16%|█▌ | 8/51 [00:07<00:43, 1.01s/it]\n 18%|█▊ | 9/51 [00:08<00:42, 1.01s/it]\n 20%|█▉ | 10/51 [00:09<00:41, 1.02s/it]\n 22%|██▏ | 11/51 [00:10<00:40, 1.02s/it]\n 24%|██▎ | 12/51 [00:11<00:39, 1.03s/it]\n 25%|██▌ | 13/51 [00:12<00:39, 1.03s/it]\n 27%|██▋ | 14/51 [00:13<00:38, 1.03s/it]\n 29%|██▉ | 15/51 [00:14<00:37, 1.03s/it]\n 31%|███▏ | 16/51 [00:15<00:36, 1.03s/it]\n 33%|███▎ | 17/51 [00:16<00:34, 1.03s/it]\n 35%|███▌ | 18/51 [00:17<00:33, 1.02s/it]\n 37%|███▋ | 19/51 [00:18<00:32, 1.02s/it]\n 39%|███▉ | 20/51 [00:19<00:31, 1.02s/it]\n 41%|████ | 21/51 [00:20<00:30, 1.02s/it]\n 43%|████▎ | 22/51 [00:21<00:29, 1.02s/it]\n 45%|████▌ | 23/51 [00:22<00:28, 1.02s/it]\n 47%|████▋ | 24/51 [00:23<00:27, 1.02s/it]\n 49%|████▉ | 25/51 [00:24<00:26, 1.02s/it]\n 51%|█████ | 26/51 [00:25<00:25, 1.01s/it]\n 53%|█████▎ | 27/51 [00:26<00:24, 1.01s/it]\n 55%|█████▍ | 28/51 [00:27<00:23, 1.01s/it]\n 57%|█████▋ | 29/51 [00:29<00:22, 1.01s/it]\n 59%|█████▉ | 30/51 [00:30<00:21, 1.01s/it]\n 61%|██████ | 31/51 [00:31<00:20, 1.01s/it]\n 63%|██████▎ | 32/51 [00:32<00:19, 1.01s/it]\n 65%|██████▍ | 33/51 [00:33<00:18, 1.01s/it]\n 67%|██████▋ | 34/51 [00:34<00:17, 1.01s/it]\n 69%|██████▊ | 35/51 [00:35<00:16, 1.01s/it]\n 71%|███████ | 36/51 [00:36<00:15, 1.01s/it]\n 73%|███████▎ | 37/51 [00:37<00:14, 1.01s/it]\n 75%|███████▍ | 38/51 [00:38<00:13, 1.01s/it]\n 76%|███████▋ | 39/51 [00:39<00:12, 1.00s/it]\n 78%|███████▊ | 40/51 [00:40<00:11, 1.00s/it]\n 80%|████████ | 41/51 [00:41<00:10, 1.00s/it]\n 82%|████████▏ | 42/51 [00:42<00:09, 1.00s/it]\n 84%|████████▍ | 43/51 [00:43<00:08, 1.00s/it]\n 86%|████████▋ | 44/51 [00:44<00:07, 1.00s/it]\n 88%|████████▊ | 45/51 [00:45<00:06, 1.00s/it]\n 90%|█████████ | 46/51 [00:46<00:05, 1.00s/it]\n 92%|█████████▏| 47/51 [00:47<00:04, 1.00s/it]\n 94%|█████████▍| 48/51 [00:48<00:02, 1.00it/s]\n 96%|█████████▌| 49/51 [00:49<00:01, 1.00it/s]\n 98%|█████████▊| 50/51 [00:50<00:00, 1.00it/s]\n100%|██████████| 51/51 [00:51<00:00, 1.00s/it]\n100%|██████████| 51/51 [00:51<00:00, 1.00s/it]", "metrics": { "predict_time": 55.873992, "total_time": 58.350199 }, "output": [ "https://replicate.delivery/mgxm/2c991654-9274-46a7-a5a8-19b00d00f73f/out-0.png", "https://replicate.delivery/mgxm/18536c12-a114-4fbb-9737-a262c3489d8a/out-1.png", "https://replicate.delivery/mgxm/e7fb4b0c-3274-4e07-92e4-16a17ca8e826/out-2.png", "https://replicate.delivery/mgxm/8b03f5a1-c800-4b38-a7d4-c92a9b403f38/out-3.png" ], "started_at": "2022-09-21T09:45:34.443315Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fd474nxl75hp5bbe6fpx4vjtq4", "cancel": "https://api.replicate.com/v1/predictions/fd474nxl75hp5bbe6fpx4vjtq4/cancel" }, "version": "3554d9e699e09693d3fa334a79c58be9a405dd021d3e11281256d53185868912" }Generated inUsing seed: 52884 0%| | 0/51 [00:00<?, ?it/s] 2%|▏ | 1/51 [00:00<00:19, 2.60it/s] 4%|▍ | 2/51 [00:01<00:37, 1.30it/s] 6%|▌ | 3/51 [00:02<00:42, 1.13it/s] 8%|▊ | 4/51 [00:03<00:43, 1.07it/s] 10%|▉ | 5/51 [00:04<00:44, 1.03it/s] 12%|█▏ | 6/51 [00:05<00:44, 1.01it/s] 14%|█▎ | 7/51 [00:06<00:44, 1.00s/it] 16%|█▌ | 8/51 [00:07<00:43, 1.01s/it] 18%|█▊ | 9/51 [00:08<00:42, 1.01s/it] 20%|█▉ | 10/51 [00:09<00:41, 1.02s/it] 22%|██▏ | 11/51 [00:10<00:40, 1.02s/it] 24%|██▎ | 12/51 [00:11<00:39, 1.03s/it] 25%|██▌ | 13/51 [00:12<00:39, 1.03s/it] 27%|██▋ | 14/51 [00:13<00:38, 1.03s/it] 29%|██▉ | 15/51 [00:14<00:37, 1.03s/it] 31%|███▏ | 16/51 [00:15<00:36, 1.03s/it] 33%|███▎ | 17/51 [00:16<00:34, 1.03s/it] 35%|███▌ | 18/51 [00:17<00:33, 1.02s/it] 37%|███▋ | 19/51 [00:18<00:32, 1.02s/it] 39%|███▉ | 20/51 [00:19<00:31, 1.02s/it] 41%|████ | 21/51 [00:20<00:30, 1.02s/it] 43%|████▎ | 22/51 [00:21<00:29, 1.02s/it] 45%|████▌ | 23/51 [00:22<00:28, 1.02s/it] 47%|████▋ | 24/51 [00:23<00:27, 1.02s/it] 49%|████▉ | 25/51 [00:24<00:26, 1.02s/it] 51%|█████ | 26/51 [00:25<00:25, 1.01s/it] 53%|█████▎ | 27/51 [00:26<00:24, 1.01s/it] 55%|█████▍ | 28/51 [00:27<00:23, 1.01s/it] 57%|█████▋ | 29/51 [00:29<00:22, 1.01s/it] 59%|█████▉ | 30/51 [00:30<00:21, 1.01s/it] 61%|██████ | 31/51 [00:31<00:20, 1.01s/it] 63%|██████▎ | 32/51 [00:32<00:19, 1.01s/it] 65%|██████▍ | 33/51 [00:33<00:18, 1.01s/it] 67%|██████▋ | 34/51 [00:34<00:17, 1.01s/it] 69%|██████▊ | 35/51 [00:35<00:16, 1.01s/it] 71%|███████ | 36/51 [00:36<00:15, 1.01s/it] 73%|███████▎ | 37/51 [00:37<00:14, 1.01s/it] 75%|███████▍ | 38/51 [00:38<00:13, 1.01s/it] 76%|███████▋ | 39/51 [00:39<00:12, 1.00s/it] 78%|███████▊ | 40/51 [00:40<00:11, 1.00s/it] 80%|████████ | 41/51 [00:41<00:10, 1.00s/it] 82%|████████▏ | 42/51 [00:42<00:09, 1.00s/it] 84%|████████▍ | 43/51 [00:43<00:08, 1.00s/it] 86%|████████▋ | 44/51 [00:44<00:07, 1.00s/it] 88%|████████▊ | 45/51 [00:45<00:06, 1.00s/it] 90%|█████████ | 46/51 [00:46<00:05, 1.00s/it] 92%|█████████▏| 47/51 [00:47<00:04, 1.00s/it] 94%|█████████▍| 48/51 [00:48<00:02, 1.00it/s] 96%|█████████▌| 49/51 [00:49<00:01, 1.00it/s] 98%|█████████▊| 50/51 [00:50<00:00, 1.00it/s] 100%|██████████| 51/51 [00:51<00:00, 1.00s/it] 100%|██████████| 51/51 [00:51<00:00, 1.00s/it]
Want to make some of these yourself?
Run this model